> Is this even practically possible? How do you re-train a blob you know
> nothing about? To me this sounds similar to saying a compiled binary
> is free software if the license allows you to decompile it and
> deobfuscate it.

If you know how to convert the blob to weights in the neural network
(something the program has to do to make any use of the blob) and know
the error function, you can continue the training with new data.

This is not any different from training the model from scratch. In both
cases we begin with some set of initial weights for a huge polynomial,
take a sample of our training data, compute the polynomial for it and
tweak the weights a bit if the result was not what we wanted. The only
difference is that when training from scratch we begin with very bad
guesses for all the weights. When we are tuning the blob we begin with
much better guesses that are closer to the values we would actually
want.

The difference to a compiled binary program is that you would want to
edit it in the source code form. You really would not want to edit the
neural network by editing the original training data and retraining the
entire network from scratch. The data set probably contains thousands,
tens of thousands or even more random pictures that you would have to go
through and see if they represent the data and results you want. It
would be much easier to test whether the network gives the correct
results and train it with new data that you know describes your problem
better.